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AI Specialist - Trainee

Beinex • Internship • Remote (US) • 9m ago

Highlights

Experience

Freshers

Joining Date

Immediate or max 30 days’ Notice Period

Qualification

Graduates in any domain who are passionate about AI

Job Description

Beinex is seeking passionate and driven individuals to join our AI Engineering team as interns. This opportunity is ideal for candidates eager to immerse themselves in the domains of Generative AI and Large Language Models (LLMs). The internship offers a structured learning experience featuring capstone projects, hands-on lab sessions, and continuous exposure to industry-leading practices and emerging technologies.

Responsibilities

- Capstone Projects: Lead or co-develop a final project, such as an LLM-powered application, AI agent, or RAG pipeline, that you'll present in a showcase.
- Hands-On Labs: Participate in guided lab sessions covering prompt engineering, vector databases, LLM deployments, multi-agent setups, fine-tuning strategies, and more.
- Model Development & Optimization: Assist in constructing, refining, and deploying LLM-based solutions, covering prompt design, RAG integrations, and agent workflows using tools like LangChain, Hugging Face, or FastAPI.
- Trend Monitoring & Evaluation: Stay updated on evolving AI engineering trends (small models, locally hosted LLMs, and multimodal systems), evaluate emerging tools and APIs, and propose potential integrations.
- Collaboration: Work closely with engineers, product managers, and domain leads to align AI solutions with technical and business needs

Key Skills Requirements

- LLM/Gen-AI Familiarity: Basic understanding of large language models, prompt engineering, vector-based retrieval, simple fine-tuning, or RAG pipelines.
- Python Skills (Not mandatory): Familiarity with Python (or similar scripting languages) and libraries like Hugging Face, PyTorch, LangChain, LangGraph, AutoGen, or FastAPI is advantageous.
- Deep Passion for Learning: Demonstrated curiosity and self-driven interest in generative AI, emerging trends, and hands-on experimentation with new tools.
- Analytical & Problem-Solving Mindset: Strong ability to break down problems, analyse model outputs, troubleshoot, and propose iterative improvements.

Additional Requirements

Nice-to-Haves

Experience with AI frameworks (LangChain, AutoGen, LLamaindex) or cloud platforms (AWS, GCP, Azure).
Familiarity with version control systems (Git).
Previous AI/ML project or coursework involving LLMs, RAG systems, or multi-agent workflows.

What You’ll Gain

Mentor-supported development across the AI tech stack, from data prep and lab exercises to deployment and capstone presentation.
In-depth exposure to current AI engineering methodologies, including prompt engineering, model optimization, RAG, LLMOps, and trending small or local LLMs.
A hands-on portfolio piece to showcase your capabilities in AI engineering.